As animation and digital technology have moved forward the interface or interaction between a human user and a computer or digital entity has developed significantly. A human-like machine or computer system able to process information intelligently, interact and present itself in a human-like manner is desirable. This is in part because human users interact better with human-like systems and/or robots. Secondly a more human-like system may have more realistic actions, responses and animations, thus reducing perceived technology barriers including the uncanny valley effect.
Animations of this type present a number of significant technical problems. Firstly, the human-like or animal-like function needs to be modelled, which in itself is extremely challenging. Then there is the challenge of taking the human-like function and using it to create a visual or graphical response that is believable to a user or viewer. One example of a difficult response is facial expression. If the system is one which interacts with a user i.e. is interactive, then there is the additional challenge of processing visual and/or audio input data.
These challenges present technical problems. The human-like models need to be integrated with graphics, animation and sensors in such a way that the system is flexible (it may need to be changed depending on the required application) and usable by a programmer/developer (the systems should be relatively intuitive or at least capable of being generally understood by a programmer) while also being able to be compiled and run efficiently.
Existing systems do not adequately address these problems. Some known systems are discussed below.
Animation Type Programs
The controls systems and signal processing fields have produced visual programming languages such as Simulink™ and VisSim™. The use of these visual systems has broadened into other fields as the systems provide an effective way to create a system and have programming code automatically generated. In a typical example a Simulink system may be built by connecting a series of block units (the block units representing for example an electrical component or group of electrical components) so as to link inputs and outputs as desired. This system is then compiled by evaluating the block structure and system attributes, reconstructing the model in a flattened structure, and ordering the block operations. In this sense the visual design is being used to create an understandable view of the model. However the model is operating in an ordered and centralised manner. Similar visual type programs are also known to make coding or circuit arrangement more straightforward.
Animation and 3D drawing programs are also known, for example Autodesk Maya™ uses node graph architecture to represent complex 3D graphics. Autodesk Maya allows animations to be produced and structured over multiple different levels. Instructions may then be supplied to the animation to encourage interaction with an environment. Some programs interface between animation and functional aspects including Max™ visual programming using Jitter. In these cases the graphics engine is substantially separate from, but controlled by, some other program or means (such as sound for Jitter). In other cases the complexity of animation simulations is overcome through the use of a limited set of possible actions. For example Havok Animation Studio™ (HAS) provides efficient character animation through the use of finite state machines (FSM). With the university of Southern California's (USCs) institute for creative technologies' (ICTs) Virtual Human toolkit, Cerebella, automatic generation of animated physical behaviours can be generated base upon accompanying dialogue however the Cerebella requires the input of detailed information about a character's mental state to create a suitable animation.
Neural Models Systems
Neural network based models, including programs such as SNNS and Emergent provide a variety of neural network environments. In different programs the models may provide biological type neurons or may build artificial neural networks. An effective neural network may contain many hundreds or thousands of neurons to simulate even straightforward models. The complexity in using large neural networks led to attempts to build artificial intelligence (AI) based devices. Social or personal robots, such as those developed by MIT Leonardo, appear to have human-like qualities. However they must be programmed in rigid and inflexible manners, typically they require specific implementation of possible actions and are dependent on certain hardware or inflexible.
Artificial Intelligent Robots
Neuro-robotic and/or brain based devices attempt to produce human like systems by copying brain based functions to create desired interactions. These models are typically very large, replicating complete brain systems from low level neurons and linking systems with biological-like interface systems. Brain based devices are robots built to emulate behaviour generated by nervous systems. These typically attempt to have human-like actions and an array of sensors but do not provide an interactive experience through interaction with humans. Brain based devices are designed for particular robots or applications and typically lack broad support for a range of different operations.
In summary known systems do not have the ability to adequately perform one or more of the following:                accommodate multiple models having different levels of simulation detail;        perform high level and low level simulations;        integrate and prioritise animation and graphics as part of the simulation;        provide visual or animated outputs of multiple models that may together comprise the simulated system;        provide an environment which has the required flexibility to adjust, remove or replicate model components;        provide an environment which is readily understandable to a modeller or developer        provide an animation system based on biological neural systems.        provide learning abilities        